Quantum kernel subspace alignment for unsupervised domain adaptation
Authors: 
Xi He, Feiyu DuAuthors Info & Claims
Xi He, Feiyu DuAuthors Info & ClaimsAbstract
Domain adaptation (DA), the sub-realm of the transfer learning, attempts to deal with machine learning tasks on an unprocessed data domain with the different, but related labeled source domain. However, the classical DA can not efficiently deal with the cross-domain tasks in quantum mechanical scenarios. In this paper, the quantum kernel subspace alignment algorithm is proposed to achieve the procedure of DA by extracting the non-linear features with the quantum kernel method and aligning the two domains with the unitary evolution. The method presented in our work can be implemented on the universal quantum computer with the quantum basic linear algebra subroutines. Based on the algorithmic complexity analysis, the procedure of the QKSA can be implemented with at least quadratic quantum speedup compared with the classical DA algorithms.
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Index Terms
- Quantum kernel subspace alignment for unsupervised domain adaptation
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
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Published: 29 May 2023
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- Natural Science Basic Research Program of Shaanxi
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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